1 to 25 of 124 Amazon SageMaker Jobs

Associate Cloud Consultant, Data AnaIytics, AWS Professional Services, Public Sector Job ID: Amazon Web Services, Inc. - A97 At Amazon Web Services (AWS), we're hiring highly technical Data Analytics consultants to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of … travel in their contract. Key job responsibilities In this role, you will work with our partners, customers and focus on our AWS offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon SageMaker, Amazon Bedrock, Amazon Q, and Amazon … ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of modern data services like DynamoDB, Amazon Kinesis, Apache Kafka, Apache Spark, Amazon Sagemaker, Amazon RDS, technologies and other industry data structures and standards. A day in the More ❯

help them achieve business outcomes with AWS. Our projects are often unique, one-of-a-kind endeavors that no one has done before. At Amazon Web Services (AWS), we are helping large enterprises build AI solutions on the AWS Cloud. We apply predictive technology to large volumes of data … our customers. You will leverage the global scale, elasticity, automation, and high-availability features of the AWS platform. You will build customer solutions with Amazon SageMaker, Amazon Bedrock, Amazon Elastic Compute (EC2), Amazon Data Pipeline, Amazon S3, Glue, Amazon DynamoDB, Amazon Relational … Database Service (RDS), Amazon Elastic Map Reduce (EMR), Amazon Kinesis, AWS Lake Formation, and other AWS services. You will collaborate across the whole AWS organization, with other consultants, customer teams, and partners on proof-of-concepts, workshops, and complex implementation projects. You will innovate and experiment to help More ❯

the latest data analytics technologies? Would you like a career path that enables you to progress with the rapid adoption of cloud computing? At Amazon Web Services, we're hiring highly technical cloud architect specialised in data analytics to collaborate with our customers and partners to derive business value … Key job responsibilities Expertise - Collaborate with pre-sales and delivery teams to help partners and customers learn and use services such as AWS Glue, Amazon S3, Amazon DynamoDB, Amazon Relational Database Service (RDS), Amazon Elastic Map Reduce (EMR), Amazon Kinesis, Amazon Redshift, Amazon Athena, AWS Lake Formation, Amazon DataZone, Amazon SageMaker and Amazon Quicksight. Solutions - Deliver technical engagements with partners and customers. This includes participating in pre-sales visits, understanding customer requirements, creating consulting proposals and creating packaged data analytics service offerings. Delivery - Engagements include projects proving the More ❯

team this SA will join supports Air Force Information, Cyber, and Intelligence customers, Combatant Commands, and Navy. This is an excellent opportunity to join Amazon's truly innovative technical teams, while also developing your skills and furthering your career within one of the most innovative and progressive technology companies … anywhere. In this role, you will have the opportunity to help shape and deliver on a strategy to build broad use of Amazon's computing web services (e.g., Amazon Bedrock, Amazon SageMaker, Amazon S3, Amazon EC2, and AWS Lambda) directly with customers and our … security - Economic and business - RFP/Acquisition support; technical architecture; understanding market trends; cost benefit analysis - Advisory/Consulting experience with Defense Department customers Amazon aims to be the most customer centric company on earth. Amazon Web Services (AWS) provides a highly reliable, scalable, low-cost infrastructure platform More ❯

career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's … experience with model customization techniques such as fine-tuning, continued pre-training, and LLM-as-judge evaluation - Experience with optimization of models on GPUs, Amazon Silicon, or TPUs, also experience with open source frameworks for building applications powered by LLMs like LangChain, LlamaIndex, and/or similar tools - Experience … building generative AI applications on AWS using services such as Amazon Bedrock and Amazon SageMaker Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value More ❯

potential and challenges of GenAI models and applications to engineering teams and C-Level executives. This requires deep familiarity across the stack - compute infrastructure (Amazon EC2, Amazon EKA), ML frameworks PyTorch, JAX, orchestration layers Kubernetes and Slurm, parallel computing (NCCL, MPI), MLOPs, through to Amazon SageMaker Hyperpod, Amazon Bedrock as well as target use cases in the cloud. This is an opportunity to be at the forefront of technological transformations, as a key technical leader. Additionally, you will work with the AWS Generative AI and EC2 product teams to shape product vision and prioritize … career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's More ❯

of vector databases (e.g., Qdrant, Pinecone) and semantic search techniques. Use of MLOps tools for CI/CD pipelines in AI (e.g., MLflow, Kubeflow, SageMaker). AI for Systems Engineering Experience working with SysML, MBSE tools, or digital engineering pipelines. Understanding of how to map or extract system design More ❯

Science or related fields. At least 2 years of experience in machine learning and data science and analytics. Proficiency in using JupyterLab or AWS Sagemaker for interactive data analysis and model development. Strong proficiency in Python and experience with libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy and More ❯

Azure. Experience with business intelligence tools like Tableau or PowerBI. Experience working with LLMs. Experience working with AWS Services like EC2, RDS(Postgres), SQS, Sagemaker, MLflow, S3, API gateway, ECS. Experience in UI frameworks like VueJS is a plus. About Us FactSet creates flexible, open data and software solutions More ❯

Electrical Engineering, Computer Engineering or related field. Experience in containerization - Docker/Kubernetes. Experience in AWS cloud and services (S3, Lambda, Aurora, ECS, EKS, SageMaker, Bedrock, Athena, Secrets Manager, Certificate Manager etc.) Proven DevOps/MLOps experience provisioning and maintaining infrastructure leveraging some of the following: Terraform, Ansible, AWS More ❯

3+ years of experience in machine learning operations, data engineering, or related roles AWS Proficiency: Strong understanding of AWS services (e.g., EC2, S3, Lambda, SageMaker, ECS) and cloud infrastructure management Programming and ML Frameworks: Proficiency in Python and experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch More ❯

accessing and processing data (PostgreSQL preferred but general SQL knowledge is more important). Familiarity with latest Data Science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g. Tensorflow, MXNet, scikit-learn). Knowledge of software engineering practices (coding practices to DS, unit testing, version control, code review). More ❯

Proficiency in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI). What we offer Culture of caring: At GlobalLogic, we prioritize a culture of caring. More ❯

data manipulation and pipeline libraries (i.e. Pandas, Polars, matplotlib, Plotly, numpy, scipy, etc.)Experience with data science environments (e.g. Jupyter Notebook, Data Bricks, or Amazon Sage Maker) Experience with Python, R, and/or Java programming languages Experience implementing the data science process, developing experiments, reporting and explaining results More ❯

Proficiency in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI). What We Offer Culture of Caring: At GlobalLogic, we prioritize a culture of caring. More ❯

Planning Analytics View more categories View less categories Sector Data Science ,Technology Role Analyst Contract Type Permanent Hours Full Time DESCRIPTION The goal of Amazon Logistics (AMZL) is to build a world class last mile operation. Amazon aims to exceed the expectations of our customers by ensuring that … tools experience: Quicksight/Tableau or similar tools 3. Scripting Experience: R/Python/C++ 4. (Optional) Experience with AWS solutions (S3, Athena, Sagemaker) Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions … priority for Amazon. Please consult our Privacy Notice () to know more about how we collect, use and transfer the personal data of our candidates. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive More ❯

the RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure More ❯

and Node.js; integrate machine learning models with ML engineers. • Design and optimize serverless and containerized architectures on AWS (Lambda, API Gateway, ECS, DynamoDB, S3, SageMaker). • Deploy, monitor, and maintain AWS infrastructure, ensuring security and compliance with government and defense standards. • Build and enhance Windows desktop applications (Electron experience More ❯

a programming challenge during the interview process. Experience with Jupyter Notebooks, Python Data Science Libraries. Experience with ML-OPS and related tools (e.g., MLFLOW, Sagemaker, Bedrock) and ability to build interactive, insightful dashboards for monitoring ML models. Place of Performance: Hybrid work in San Antonio, TX. Desired Skills (Optional More ❯

language models Proven experience with cloud platforms such as AWS, Azure, or Google Cloud Familiarity with tools and frameworks such as TensorFlow, PyTorch, MLflow, SageMaker, or Databricks Deep understanding of data architecture, APIs, and model deployment best practices Knowledge of MLOps and full model lifecycle management Excellent communication and More ❯

and deploy AI and machine learning models and frameworks, such as TensorFlow, PyTorch, MXNet, etc., using cloud-based platforms and services, such as AWS SageMaker, AWS AI Services, etc. Introduce CI/CD pipelines that provide rapid feedback and allow continuous development and delivery. Optimize and monitor the performance More ❯

be fluent in Python - writing readable, testable, and maintainable code comes naturally to you. You'll be comfortable with cloud-based tools like AWS SageMaker and Lambda, and you'll know your way around Git, SQL, and common Python data science libraries (like pandas/polars, scikit-learn, xgboost More ❯

Have: Experience with deploying analytics workloads on platform as a service ( PaaS ) and sof tware as a service ( SaaS ) , including AWS EMR, Redshift, or SageMaker or Azure Databricks, SQL Data Warehouse, or Machine Learning service Experience with distributed or parallel programming frameworks, including Apache Spark or NVIDIA CUDA, and More ❯

techniques, and evaluation strategies . Experience in programming languages such as Python or Java . Understanding of cloud platforms ( Google Cloud , AWS , Vertex AI , Sagemaker ). Bonus points: Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn. Experience with data analysis or data science. The experience, skills, and More ❯

Required Experience • Bachelor's degree in computer science or related engineering field with 0-2 years of technical experience • Development using Java and AWS SageMaker to create repeatable training workflows that accelerate model development • Provisioning, operating, and maintaining systems running on AWS (or equivalent cloud providers) • AI/ML More ❯